The article argues that large language models (LLMs) struggle with causal reasoning and long-term planning due to their reliance on sequence prediction. It suggests that world models, which incorporate latent dynamics, could significantly enhance performance in these areas, as demonstrated by a new environment called Flux that shows LLMs achieving only an 11% win rate compared to 79% for agents using explicit state tracking.